Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring

TL;DR

This paper introduces a knowledge distillation framework that transfers Chain-of-Thought reasoning from large language models to smaller models, improving their performance in industrial asset health monitoring tasks.

Contribution

It presents a novel CoT distillation method for SLMs in industrial applications, enhancing reasoning capabilities and decision-making accuracy.

Findings

Fine-tuned SLMs outperform base models significantly.

Distilled SLMs narrow the performance gap with LLMs.

Benchmark results show improved reasoning in industrial asset health tasks.

Abstract

Small Language Models (SLMs) are becoming increasingly popular in specialized fields, such as industrial applications, due to their efficiency, lower computational requirements, and ability to be fine-tuned for domain-specific tasks, enabling accurate and cost-effective solutions. However, performing complex reasoning using SLMs in specialized fields such as Industry 4.0 remains challenging. In this paper, we propose a knowledge distillation framework for industrial asset health, which transfers reasoning capabilities via Chain-of-Thought (CoT) distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs). We discuss the advantages and the process of distilling LLMs using multi-choice question answering (MCQA) prompts to enhance reasoning and refine decision-making. We also perform in-context learning to verify the quality of the generated knowledge and benchmark the performance of fine-tuned SLMs with generated knowledge against widely used LLMs. The results show that the fine-tuned SLMs with CoT reasoning outperform the base models by a significant margin, narrowing the gap to their LLM counterparts. Our code is open-sourced at: https://github.com/IBM/FailureSensorIQ.